There are other ways of interpreting value stability; a satisficer is one example. But those don't tend to be stable
That statement does not make sense. I hope if you read it with a fresh mind you can see why. "There are other ways of defining stable, but they are not stable." Perhaps you need to taboo the word stable here?
And would those defaults and update procedures remain stable themselves?
No, and that's the whole point! Stability is scary. Stability leads to Clippy. People wouldn't want stable. They'd want sensible. Sensible updates its behavior based on new information.
Perhaps you need to taboo the word stable here?
"There are some agents that are defined to have constant value systems, where, nonetheless, the value system will drift in practice".
Stability leads to Clippy.
There are many bad stable outcomes. And an unstable update system will eventually fall into one of them, because they're attractor states. To avoid this, you need to define "sensible" in such a way as the agent never enters such states. You've effectively promoting a different kind of goal stability - a zone of stability, rathe...
I'm soon going to go on a two day "AI control retreat", when I'll be without internet or family or any contact, just a few books and thinking about AI control. In the meantime, here is one idea I found along the way.
We often prefer leaders to follow deontological rules, because these are harder to manipulate by those whose interests don't align with ours (you could say the similar things about frequentist statistics versus Bayesian ones).
What about if we applied the same idea to AI control? Not giving the AI deontological restrictions, but programming with a similart goal: to prevent a misalignment of values to be disastrous. But who could do this? Well, another AI.
My rough idea goes something like this:
AI A is tasked with maximising utility function u - a utility function which, crucially, it doesn't know yet. Its sole task is to create AI B, which will be given a utility function v and act on it.
What will v be? Well, I was thinking of taking u and adding some noise - nasty noise. By nasty noise I mean v=u+w, not v=max(u,w). In the first case, you could maximise v while sacrificing u completely, it w is suitable. In fact, I was thinking of adding an agent C (which need not actually exist). It would be motivated to maximise -u, and it would have the code of B and the set of u+noise, and would choose v to be the worst possible option (form the perspective of a u-maximiser) in this set.
So agent A, which doesn't know u, is motivated to design B so that it follows its motivation to some extent, but not to extreme amounts - not in ways that might sacrifice some of the values of some sub-part of its utility function, because that might be part of the original u.
Do people feel this idea is implementable/improvable?